From 0fcc1aabaaa2487fdab2e95035b4747859816796 Mon Sep 17 00:00:00 2001 From: Li Fuchen Date: Mon, 23 Sep 2019 16:37:08 +0800 Subject: [PATCH] Fixed issue with train_dyn_rnn.py training report nan (#808) Fixed issue with train_dyn_rnn.py training report nan --- 06.understand_sentiment/train_dyn_rnn.py | 34 ++++-------------------- 1 file changed, 5 insertions(+), 29 deletions(-) diff --git a/06.understand_sentiment/train_dyn_rnn.py b/06.understand_sentiment/train_dyn_rnn.py index 54f1b53..666bd38 100644 --- a/06.understand_sentiment/train_dyn_rnn.py +++ b/06.understand_sentiment/train_dyn_rnn.py @@ -44,35 +44,11 @@ def parse_args(): def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size): emb = fluid.layers.embedding( input=data, size=[input_dim, emb_dim], is_sparse=True) - sentence = fluid.layers.fc(input=emb, size=lstm_size, act='tanh') - - rnn = fluid.layers.DynamicRNN() - with rnn.block(): - word = rnn.step_input(sentence) - prev_hidden = rnn.memory(value=0.0, shape=[lstm_size]) - prev_cell = rnn.memory(value=0.0, shape=[lstm_size]) - - def gate_common(ipt, hidden, size): - gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True) - gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False) - return gate0 + gate1 - - forget_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, - lstm_size)) - input_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, - lstm_size)) - output_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, - lstm_size)) - cell_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden, - lstm_size)) - - cell = forget_gate * prev_cell + input_gate * cell_gate - hidden = output_gate * fluid.layers.tanh(x=cell) - rnn.update_memory(prev_cell, cell) - rnn.update_memory(prev_hidden, hidden) - rnn.output(hidden) - - last = fluid.layers.sequence_last_step(rnn()) + sentence = fluid.layers.fc(input=emb, size=lstm_size * 4, act='tanh') + + lstm, _ = fluid.layers.dynamic_lstm(sentence, size=lstm_size * 4) + + last = fluid.layers.sequence_last_step(lstm) prediction = fluid.layers.fc(input=last, size=class_dim, act="softmax") return prediction -- GitLab